Gene’s Background

Bio:

Gene’s Ph.D. dissertation  was testing the theory we are treated differently based on what we look like and how much our parent(s) make.  In result, we see the world differently and respond to various stimuli differently, therefore when we take our homes to college/universities, we utilize and respond to various services in higher education differently which will yield varying academic performances.  In short, it was regression modeling.

Gene comes from a low-income immigrant background: it took him nearly 19 years to get his U.S. permanent residency and nearly 23 years to get his U.S. Citizenship.  Gene had endured experiences much harder than school.   The first 19 years in United States was so painful and traumatic, he always refers his life events prior to November 1999 and after November 1999.

Gene is a former Assistant Dean of Academic Affairs in College of Letter and Science at the University of Wisconsin-Madison until his wife became an associate professor of history at the University of Washington-Seattle.  He is a diversity officer first and data professional second.  The first 20 years of Gene’s work with TRIO population group(s) were composed of teaching, advising and mentoring undergraduate and graduate students, and obviously much work in administration/program(s) management.   His service and graduate work (including his Ph.D. dissertation on regression modeling on TRIO Student Support Services back in 1999) was on first-generation, low-income, and historically underrepresented population groups since 1995 as an instructor of TRIO Student Support Services in UW-Madison.   Gene ran the McNair Scholars Program in both UW-Madison and UW-Seattle, which is a Ph.D. preparatory program to successfully transition first-generation, low-income, and underrepresented students from BA/BS to Ph.D. programs via undergraduate research, seminars, and pre-graduate advising.  Gene loves simplifying and clarifying “messy and complex” information and examining its patterns, but also identifying exceptions to those various patterns.  In relation to identifying exception to patterns, he is careful NOT to utilize machine learning algorithm as a quantitative profiling tool, rather than a very accurate approximation tool to predict probable future events which will maximally benefit and minimally harm all involved.

Gene started his journey with the TRIO population groups under the mentorship and training from Dr. Ruttanatip Chonwerawong(Brunner), Dr. Brenda Pfaeler, and Dean Walter Lane, at the University of Wisconsin-Madison School of Education which changed his life forever: He is ever so grateful for the opportunities and their teaching/training they provided for him as well as their dedication to first-generation, low-income, and underrepresented students as well as student with disabilities.

Gene started his adventures in statistics similar to a lost tourist in the 1990s with XLISPSTAT, Minitab, and S-PLUS, at the University of Wisconsin-Madison, where he took his share of bumps and bruises of anxiety and confusion and thought he was divorced from statistics and data for good upon graduation after becoming dazed and confused.  However,  as an administrator, explaining the disparities among ethnicities/race, gender, disabilities, and socioeconomic status, application knowledge of statistics/data became a must.  And illustrations through data was one of the most effective ways (sometimes the only way) to research, improve, prove and persuade for better opportunities and hope for our students.  He was relieved and delighted (and sometimes terrified) when  R  and Python programming language came about which made him and his research team happier and more efficient .  He feels very fortunate to receive his training from late Professor Norman Draper, Professor Wei-Yin Loh , and Professor Bob Wardrop from the University of Wisconsin-Madison Department of Statistics, as well as  Mr. Ivan Pagan in University of Wisconsin-Madison Business Learning Center.   And when Gene started his data adventure in University of Washington-Seattle, he owes a great deal to Carol Diem  and Arlyn Arquiza who trained him in UW data infrastructure since 2006/2007.